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Adversary Detection For Cognitive Radio NetworksPreliminaries of Analytical Tools

Adversary Detection For Cognitive Radio Networks: Preliminaries of Analytical Tools [This chapter mainly focuses on reviewing some of the important analytic tools used in existing literature for adversary detection in CR networks. Particularly, in the first part of the chapter, two widely employed statistical inference tools, sequential hypothesis testing, and belief propagation are reviewed. In the second part of this chapter, some important machine learning methods are reviewed, including non-parametric Bayesian classification, artificial neural network, and affinity propagation. Throughout the discussions of this chapter, we will focus on introducing the relevant concepts and models, algorithmic procedure as well as important properties of these analytic tools, whereas their applications in adversary detection will be postponed to the next chapter.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adversary Detection For Cognitive Radio NetworksPreliminaries of Analytical Tools

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2018
ISBN
978-3-319-75867-1
Pages
7 –17
DOI
10.1007/978-3-319-75868-8_2
Publisher site
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Abstract

[This chapter mainly focuses on reviewing some of the important analytic tools used in existing literature for adversary detection in CR networks. Particularly, in the first part of the chapter, two widely employed statistical inference tools, sequential hypothesis testing, and belief propagation are reviewed. In the second part of this chapter, some important machine learning methods are reviewed, including non-parametric Bayesian classification, artificial neural network, and affinity propagation. Throughout the discussions of this chapter, we will focus on introducing the relevant concepts and models, algorithmic procedure as well as important properties of these analytic tools, whereas their applications in adversary detection will be postponed to the next chapter.]

Published: Mar 8, 2018

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